Bayesian Persuasion under Ex Ante and Ex Post Constraints
Authors: Yakov Babichenko, Inbal Talgam-Cohen, Konstantin Zabarnyi5127-5134
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a simple way to mathematically model such constraints as restrictions on Receiver s admissible posterior beliefs. We consider two families of constraints ex ante and ex post; the latter limits each instance of Sender-Receiver communication, while the former more general family can also pose restrictions in expectation. For the ex ante family, a result of Doval and Skreta (2018) establishes the existence of an optimal signaling scheme with a small number of signals at most the number of constraints plus the number of states of nature and we show this result is tight. For the ex post family, we tighten the previous bound of Vølund (2018), showing that the required number of signals is at most the number of states of nature, as in the original Kamenica-Gentzkow setting. As our main algorithmic result, we provide an additive bi-criteria FPTAS for an optimal constrained signaling scheme assuming a constant number of states of nature; we improve the approximation to single-criteria under a Slater-like regularity condition. The FPTAS holds under standard assumptions, and more relaxed assumptions yield a PTAS. We then establish a bound on the ratio between Sender s optimal utility under convex ex ante constraints and the corresponding ex post constraints. |
| Researcher Affiliation | Academia | Yakov Babichenko, Inbal Talgam-Cohen, Konstantin Zabarnyi Technion Israel Institute of Technology yakovbab@technion.ac.il, italgam@cs.technion.ac.il, konstzab@gmail.com |
| Pseudocode | Yes | Algorithm 1 Ex ante to ex post |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only mentions the full paper version available on arXiv. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets for training or evaluation. It discusses applications as motivating examples, but no specific dataset is used. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments that would require dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers, nor does it list self-contained solvers or environments with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setups with concrete hyperparameter values or training configurations. |